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project_6/cccl_upstream/cudax/examples/vector.cuh
EngineX CI 56fd68e7dd [INFRA] Import NVIDIA/CCCL upstream as optimization reference library
CCCL (CUDA C++ Core Libraries) provides:
- CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk)
- Thrust: high-level parallel algorithms (transform_reduce, sort, scan)
- libcudacxx: CUDA C++ standard library (atomics, barriers, memory)
- cudax: experimental features (memory resources, allocators)
- Tuning policies: per-SM hardware-specific algorithm parameters

Competition optimization vectors mapped to CCCL:
- Output TPS (83% weight): warp_reduce, block_reduce, device_topk
- Input TPS (14% weight): device_scan, block_load, prefetch
- Cache TPS (3% weight): prefix caching strategy patterns
- Memory (0.9 util): pooled/cached/buddy allocators

Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only)
License: Apache-2.0
2026-07-30 09:35:51 +00:00

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//===----------------------------------------------------------------------===//
//
// Part of CUDA Experimental in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#ifndef _CUDAX__CONTAINER_VECTOR
#define _CUDAX__CONTAINER_VECTOR
#include <cuda/__cccl_config>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <cuda/__stream/stream_ref.h>
#include <cuda/std/__type_traits/maybe_const.h>
#include <cuda/std/span>
#include <cuda/experimental/__detail/utility.cuh>
#include <cuda/experimental/__launch/param_kind.cuh>
#include <cstdio>
namespace cuda::experimental
{
using ::cuda::std::span;
using ::thrust::device_vector;
using ::thrust::host_vector;
template <typename _Ty>
class vector
{
public:
vector() = default;
explicit vector(size_t __n)
: __h_(__n)
{}
_Ty& operator[](size_t __i) noexcept
{
__dirty_ = true;
return __h_[__i];
}
const _Ty& operator[](size_t __i) const noexcept
{
return __h_[__i];
}
private:
void sync_host_to_device([[maybe_unused]] ::cuda::stream_ref __str, __detail::__param_kind __p) const
{
if (__dirty_)
{
if (__p == __detail::__param_kind::_out)
{
// There's no need to copy the data from host to device if the data is
// only going to be written to. We can just allocate the device memory.
__d_.resize(__h_.size());
}
else
{
// TODO: use a memcpy async here
__d_ = __h_;
}
__dirty_ = false;
}
}
void sync_device_to_host(::cuda::stream_ref __str, __detail::__param_kind __p) const
{
if (__p != __detail::__param_kind::_in)
{
// TODO: use a memcpy async here
__str.sync(); // wait for the kernel to finish executing
__h_ = __d_;
}
}
template <__detail::__param_kind _Kind>
class __action //: private __detail::__immovable
{
using __cv_vector = ::cuda::std::__maybe_const<_Kind == __detail::__param_kind::_in, vector>;
public:
explicit __action(::cuda::stream_ref __str, __cv_vector& __v)
: __str_(__str)
, __v_(__v)
{
__v_.sync_host_to_device(__str_, _Kind);
}
__action(__action&&) = delete;
~__action()
{
try
{
__v_.sync_device_to_host(__str_, _Kind);
}
catch (const ::std::exception& e)
{
static_cast<void>(
::fprintf(stderr, "Exception occurred during host to device synchronization: %s\n", e.what()));
}
catch (...)
{
static_cast<void>(::fprintf(stderr, "Unknown exception occurred during host to device synchronization\n"));
}
}
::cuda::std::span<_Ty> transformed_argument() const
{
return {__v_.__d_.data().get(), __v_.__d_.size()};
}
private:
::cuda::stream_ref __str_;
__cv_vector& __v_;
};
[[nodiscard]] friend __action<__detail::__param_kind::_inout>
transform_launch_argument(::cuda::stream_ref __str, vector& __v)
{
return __action<__detail::__param_kind::_inout>{__str, __v};
}
[[nodiscard]] friend __action<__detail::__param_kind::_in>
transform_launch_argument(::cuda::stream_ref __str, const vector& __v)
{
return __action<__detail::__param_kind::_in>{__str, __v};
}
template <__detail::__param_kind _Kind>
[[nodiscard]] friend __action<_Kind>
transform_launch_argument(::cuda::stream_ref __str, __detail::__box<vector, _Kind> __b)
{
return __action<_Kind>{__str, __b.__val};
}
mutable host_vector<_Ty> __h_;
mutable device_vector<_Ty> __d_{};
mutable bool __dirty_ = true;
};
} // namespace cuda::experimental
#endif